Data analysis is abstract. It's not math (although there is math involved) and it's not English or accounting. It takes a practical approach to really understand the pitfalls that good analysts can fall into.

Still, most students haven't had to deal with vague parameters and large data sets by the time they get their first job, and that's a shame! Many students have never heard of a data warehouse and this is where most of the data that helps managers make critical decisions resides.

In the modern business world, data analysis is not limited to data scientists. It is also critical for analysts, systems engineers, finance teams, PR, HR, marketing, and so on.

If you liked this video, check out some of my other top videos.

Top courses for data engineers
https://www.youtube.com/watch?vkW8_l57w74g

Data engineering project ideas
https://youtu.be/LJkVvNWlO0g

If you want to learn more about machine learning, check out the DataCamps Machine Learning course

https://bit.ly/3BeLEml

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0:00 Introduction
2:15 Establish a clear data analysis process
4:56 Don't bury the Lede
6:39 Peer review of data analysis
8:51 Triple check your details
11:46 Know when to stop your analysis
13:05 Summary

Tags: Data engineering projects, Data engineer project ideas, data project resources, data analytics project resources, data project portfolio

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About me:
I have focused my career on all forms of data. I have focused on developing algorithms to detect fraud, reduce patient readmission, and redesign insurer policies to help reduce the overall cost of healthcare. I also helped develop analytics for marketing and IT operations to optimize limited resources such as employees and budget. I provide private consulting on data science and technical issues, both solo and with a company called Acheron Analytics. I have experience working hands-on with technical problems as well as helping leadership teams develop strategies to maximize their data.

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